On Accuracy Estimation Using Parametric Bootstrap in small Area Prediction Problems

Żądło Tomasz ()
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Żądło Tomasz: University of Economics in Katowice, Department of Statistics, Econometrics and Mathematics, 50, 1 Maja Street, 40-287Katowice, Poland.

Journal of Official Statistics, 2020, vol. 36, issue 2, 435-458

Abstract: We consider longitudinal data and the problem of prediction of subpopulation (domain) characteristics that can be written as a linear combination of the variable of interest, including cases of small or zero sample sizes in the domain and time period of interest. We consider the empirical version of the predictor proposed by Royall (1976) showing that it is a generalization of the empirical version of the predictor presented by Henderson (1950). We propose a parametric bootstrap MSE estimator of the predictor. We prove its asymptotic unbiasedness and derive the order of its bias. Considerations are supported by Monte Carlo simulation analyses to compare its accuracy (not only the bias) with other MSE estimators, including jackknife and weighted jackknife MSE estimators that we adapt for the considered predictor.

Keywords: Empirical best linear unbiased predictor; model approach in survey sampling; parametric bootstrap; properties of MSE estimators; small area estimation (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:36:y:2020:i:2:p:435-458:n:7

DOI: 10.2478/jos-2020-0022

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